New COVID-19 diagnoses have dropped faster than expected in the United States . Interpretations of the decrease have focused on changing factors (e.g . mask-wearing, vaccines, etc .), but predictive models largely ignore heterogeneity in behaviorally-driven exposure risks among distinct groups . We present a simplified compartmental model with differential mixing in two behaviorally distinct groups . We show how homophily in behavior, risk, and exposure can lead to early peaks and rapid declines that critically do not signal the end of the outbreak . Instead, higher exposure risk groups may more rapidly exhaust available susceptibles while the lower risk group are still in a (slower) growth phase of their outbreak curve . This simplified model demonstrates that complex incidence curves, such as those currently seen in the US, can be generated without changes to fundamental drivers of disease dynamics . Correct interpretation of incidence curves will be critical for policy decisions to effectively manage the pandemic.